1. Field of the Invention
The present invention relates to a performance predicting apparatus, a performance predicting method, and a program which predict functional performance of an object from its shape.
2. Description of Related Art
A computer supporting method is known which predicts various functional performances of an object based on design information representing the shape of the object. For example, Japanese Unexamined Patent Application, First Publication No. 2011-40054 (Patent Document 1) discloses that functional performance of an object, the design of which is changed by a computer aided design (CAD) system or the like, is calculated using an approximate model. In Japanese Unexamined Patent Application, First Publication No. 2010-61439 (Patent Document 2), a new parameter for interpolating neighboring parameters is created and it is determined whether the created parameter is proper based on solutions calculated using a plurality of objective functions for the created parameter. Japanese Unexamined Patent Application, First Publication No. 2000-339147 (Patent Document 3) discloses that one relational approximate expression is selected based on factor information representing conditions for software development out of a plurality of relational approximate expressions used to calculate an estimated workload and the estimated workload is calculated using the selected relational approximate expression. It is preferable that an approximate model have a feature value into which the shape of a predetermined part is quantified from a plurality of design information pieces used to create the approximate model as an input parameter thereof. PCT International Publication for Patent Application No. WO2008/133235 (Patent Document 4) discloses a technique of extracting feature points such as a fingerprint image through pattern matching. “Contour Based Shape Tweening by DMatching”, written by Ayako TAKABATAKE and Hironobu FUJIYOSHI, Tokai-Section Joint Conference on Electrical and Related Engineering, September, 2005 (Non-patent Document 1), discloses a technique of extracting feature points of a profile shape using DP matching.
As described above, the techniques of predicting functional performance from design information of an object using an approximate model are known. However, when the shape of an object is complex and the amount of design information is large such as when an object is a vehicle and aerodynamic performance is calculated as the functional performance, prediction and calculation of functional performance of objects having different shapes using only a single approximate model may lower prediction accuracy. Patent Documents 1 to 4 do not mention how to predict functional performance with high accuracy when the amount of design information of an object is large.
When it is intended to extract a feature value from design information, it is necessary to specify feature points which are points forming a shape represented by the feature value. As a method of extracting feature points, it can be considered that a user of a system watches design information with their eyes and specifies feature points one by one; however, it is not practical to manually extract feature points from a large amount of design information for creating an approximate model.
In the method disclosed in Patent Document 4, pixels of an image are used as elements of multidimensional data used for pattern matching. However, a cross-sectional image of a three-dimensional shape is sparse. Accordingly, when multidimensional data having pixels as elements is used, there is a problem in that the extraction accuracy of feature points is lowered.
In the method disclosed in Non-patent Document 1, the lengths of line segments connecting the center of gravity of a profile shape to points on a profile line are used as the element of multidimensional data used for pattern matching. However, in this case, there is also a problem in that an inter-pattern linking error range may be wide and satisfactory accuracy may not be obtained.
In order to enhance the accuracy of an approximate model, it is necessary to create an approximate model using as much design information representing different shapes as possible. However, since capacity of a storage device storing the design information is limited, the amount of design information which can be stored in the storage device is limited. Therefore, design information not contributing to enhancement of the accuracy of an approximate model need not be recorded in the storage device.
When it is intended to enhance approximation accuracy (accuracy of aerodynamic performance calculated by a CFD) of an approximate model, it is necessary to increase the number of types of feature values which are the parameters of the approximate model.
Accordingly, when an approximate model is created, the number of feature values constituting the approximate model increases and the time necessary for creating the approximate model also increases.
When feature values of an approximate model simply increase, none of the feature values contribute to estimation of aerodynamic performance; however, some feature values may serve as noise in estimation of aerodynamic performance.
Only feature values previously known to contribute to approximation of aerodynamic performance have been used to create the approximate model; however, the types of feature values vary depending on the structure of an object approximated by an approximate model, and thus it is not possible to set feature values contributing to aerodynamic performance in advance in creating an approximate model.
As the number of designs as samples increases, the approximation accuracy of functional performance to be estimated rises.
However, as the number of designs increases, the time necessary to create an approximate model also increases.
It is desirable that the number of samples by which an approximate model satisfies predetermined approximation accuracy is known in advance. However, since the number of samples varies depending on the complexity of an object approximated by the approximate model, the number of samples cannot be set in advance.
Therefore, when the number of feature values or samples is set to be small, the approximation accuracy of a created approximate expression is lowered. On the other hand, when the number of feature values or samples is set to large, feature values serving as noise in estimation are included and the time necessary for creating an approximate model increases unnecessarily.